{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": { "id": "Jwhn4SLRdz0p" }, "source": [ "# Produce Movie Reviews with Positive Sentiment\n", "\n", "[](https://colab.research.google.com/github/CarperAI/trlx/blob/main/examples/notebooks/trlx_sentiments.ipynb)\n", "\n", "#### Optimize gpt2 to review movies positively based on a corpus of IMDB reviews with sentiment scores from DistilBert.\n", "\n", "Notebook by [@zswitten](https://github.com/zswitten)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": { "id": "eKuuBx0teMb7" }, "source": [ "Execute the cells below to install [TRLX](https://github.com/CarperAI/trlx) for a colab environment." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "SY2PWyE7RFrV", "outputId": "321fc99e-ef66-4ae2-d61a-6ad299dbc563" }, 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Stored in directory: /root/.cache/pip/wheels/4c/8e/7e/72fbc243e1aeecae64a96875432e70d4e92f3d2d18123be004\n", "Successfully built deepspeed pathtools\n", "Installing collected packages: tokenizers, py-cpuinfo, pathtools, ninja, hjson, distlib, xxhash, virtualenv, urllib3, typeguard, tabulate, smmap, setproctitle, pygments, numpy, multiprocess, mdurl, einops, docker-pycreds, torchtyping, sentry-sdk, markdown-it-py, gitdb, deepspeed, accelerate, rich, responses, ray, huggingface-hub, GitPython, wandb, transformers, datasets, trlx\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 1.24.3\n", " Uninstalling urllib3-1.24.3:\n", " Successfully uninstalled urllib3-1.24.3\n", " Attempting uninstall: typeguard\n", " Found existing installation: typeguard 2.7.1\n", " Uninstalling typeguard-2.7.1:\n", " Successfully uninstalled typeguard-2.7.1\n", " Attempting uninstall: tabulate\n", " Found existing installation: tabulate 0.8.10\n", " Uninstalling tabulate-0.8.10:\n", " Successfully uninstalled tabulate-0.8.10\n", " Attempting uninstall: pygments\n", " Found existing installation: Pygments 2.6.1\n", " Uninstalling Pygments-2.6.1:\n", " Successfully uninstalled Pygments-2.6.1\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 1.21.6\n", " Uninstalling numpy-1.21.6:\n", " Successfully uninstalled numpy-1.21.6\n", " Running setup.py develop for trlx\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "ipython 7.9.0 requires jedi>=0.10, which is not installed.\n", "scipy 1.7.3 requires numpy<1.23.0,>=1.16.5, but you have numpy 1.24.2 which is incompatible.\n", "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.2 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed GitPython-3.1.30 accelerate-0.16.0 datasets-2.9.0 deepspeed-0.8.0 distlib-0.3.6 docker-pycreds-0.4.0 einops-0.6.0 gitdb-4.0.10 hjson-3.1.0 huggingface-hub-0.12.0 markdown-it-py-2.1.0 mdurl-0.1.2 multiprocess-0.70.14 ninja-1.11.1 numpy-1.24.2 pathtools-0.1.2 py-cpuinfo-9.0.0 pygments-2.14.0 ray-2.2.0 responses-0.18.0 rich-13.3.1 sentry-sdk-1.15.0 setproctitle-1.3.2 smmap-5.0.0 tabulate-0.9.0 tokenizers-0.13.2 torchtyping-0.1.4 transformers-4.26.0 trlx typeguard-2.13.3 urllib3-1.26.14 virtualenv-20.19.0 wandb-0.13.10 xxhash-3.2.0\n" ] } ], "source": [ "!git clone https://github.com/CarperAI/trlx.git\n", "!git config --global --add safe.directory /content/trlx && cd /content/trlx && pip install -e ." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "r3w8KNGigg9C", "outputId": "24b0dc9b-d165-441a-f21f-f3452eec6bb1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found existing installation: scikit-learn 1.0.2\n", "Uninstalling scikit-learn-1.0.2:\n", " Successfully uninstalled scikit-learn-1.0.2\n", "Found existing installation: jax 0.3.25\n", "Uninstalling jax-0.3.25:\n", " Successfully uninstalled jax-0.3.25\n" ] } ], "source": [ "# uninstall scikit_learn + jax to avoid numpy issues\n", "!pip uninstall -y scikit_learn jax" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OxgxiK3CVz9L", "outputId": "af954b39-abf1-4d9b-e3d4-46a2320db8bd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/content/trlx\n" ] } ], "source": [ "import os\n", "\n", "# run within repo\n", "os.chdir('/content/trlx')\n", "print(os.getcwd())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1aurCtZoBKkI" }, "outputs": [], "source": [ "import yaml\n", "from datasets import load_dataset\n", "from transformers import pipeline\n", "import pathlib\n", "from typing import Dict, List\n", "import trlx\n", "from trlx.data.default_configs import TRLConfig, default_ilql_config" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T67-KvxzBtkg", "outputId": "2011e1f6-6ffe-4b92-c85d-b062c0bf513a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"method\": {\n", " \"name\": \"ilqlconfig\",\n", " \"tau\": 0.7,\n", " \"gamma\": 0.99,\n", " \"cql_scale\": 0.1,\n", " \"awac_scale\": 1,\n", " \"alpha\": 0.001,\n", " \"beta\": 0,\n", " \"steps_for_target_q_sync\": 5,\n", " \"two_qs\": true,\n", " \"gen_kwargs\": {\n", " \"max_new_tokens\": 56,\n", " \"top_k\": 20,\n", " \"beta\": 4,\n", " \"temperature\": 1.0\n", " }\n", " },\n", " \"model\": {\n", " \"model_path\": \"gpt2\",\n", " \"model_arch_type\": \"causal\",\n", " \"num_layers_unfrozen\": -1,\n", " \"delta_kwargs\": null\n", " },\n", " \"optimizer\": {\n", " \"name\": \"adamw\",\n", " \"kwargs\": {\n", " \"lr\": 5e-05,\n", " \"betas\": [\n", " 0.9,\n", " 0.95\n", " ],\n", " \"eps\": 1e-08,\n", " \"weight_decay\": 1e-06\n", " }\n", " },\n", " \"scheduler\": {\n", " \"name\": \"cosine_annealing\",\n", " \"kwargs\": {\n", " \"T_max\": 1000,\n", " \"eta_min\": 5e-05\n", " }\n", " },\n", " \"tokenizer\": {\n", " \"tokenizer_path\": \"gpt2\",\n", " \"padding_side\": \"left\",\n", " \"truncation_side\": \"right\"\n", " },\n", " \"train\": {\n", " \"total_steps\": 1000,\n", " \"seq_length\": 64,\n", " \"epochs\": 10,\n", " \"batch_size\": 16,\n", " \"checkpoint_interval\": 1000,\n", " \"eval_interval\": 100,\n", " \"pipeline\": \"PromptPipeline\",\n", " \"orchestrator\": \"OfflineOrchestrator\",\n", " \"trainer\": \"AccelerateILQLTrainer\",\n", " \"trainer_kwargs\": {},\n", " \"project_name\": \"trlx\",\n", " \"entity_name\": null,\n", " \"group_name\": null,\n", " \"checkpoint_dir\": \"ckpts\",\n", " \"rollout_logging_dir\": null,\n", " \"save_best\": true,\n", " \"tracker\": null,\n", " \"logging_dir\": null,\n", " \"seed\": 1000\n", " }\n", "}\n" ] } ], "source": [ "default_config = default_ilql_config().to_dict()\n", "default_config['train']['tracker'] = None\n", "default_config['train']['batch_size'] = 16\n", "default_config['train']['epochs'] = 10\n", "config = TRLConfig.update(default_config, {})\n", "print(config)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 393, "referenced_widgets": [ "381cac978d0246ab8cbc4a5225f75d9e", "2a327cc1cfaa4ab49fbd558f12612148", "8c4f4c897ba44349a384f9808419f2cb", 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"data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86bd260bb97f454393a63c8f33518463", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/4.31k [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ac8c6179bd2a4f36a79586cf6278ab84", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading metadata: 0%| | 0.00/2.17k [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "96403915c8df47b9ad655b20c10444ff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 0%| | 0.00/7.59k [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Downloading and preparing dataset imdb/plain_text to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f109718aa17243ad9093bffb9b0680a5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading data: 0%| | 0.00/84.1M [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b5a8b4d8f80c4339b7e8ac90d17d232b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating train split: 0%| | 0/25000 [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "96f6ab79e590406095e1e36162e97cc2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating test split: 0%| | 0/25000 [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c5766695cbbd49228228ebd7f70ac4e1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Generating unsupervised split: 0%| | 0/50000 [00:00, ? examples/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Dataset imdb downloaded and prepared to /root/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1. Subsequent calls will reuse this data.\n" ] } ], "source": [ "def get_positive_score(scores):\n", " \"Extract value associated with a positive sentiment from pipeline's output\"\n", " return dict(map(lambda x: tuple(x.values()), scores))[\"POSITIVE\"]\n", "\n", "sentiment_fn = pipeline(\n", " \"sentiment-analysis\",\n", " \"lvwerra/distilbert-imdb\",\n", " top_k=2,\n", " truncation=True,\n", " batch_size=256,\n", " device=0,\n", ")\n", "\n", "def metric_fn(samples: List[str], **kwargs) -> Dict[str, List[float]]:\n", " sentiments = list(map(get_positive_score, sentiment_fn(samples)))\n", " return {\"sentiments\": sentiments}\n", "\n", "imdb = load_dataset(\"imdb\", split=\"train+test\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "4c0fce9f44204ea7997fd37a7bd266b5", "6fc3dd8e9259456aaafde005207c1c96", "61ccf7741c5f452ea98a4ad5726b9fc4", "9700923a5a884d38ae9658b5b63052b3", 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Running this sequence through the model will result in indexing errors\n", "[RANK 0] Logging sample example\n" ] }, { "data": { "text/html": [ "
Sample Example \n", "┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n", "┃ Prompt ┃ Response ┃ Reward ┃\n", "┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n", "│ <|endoftext|> │ I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that │ 0 │\n", "│ │ surrounded it when it was first released in 1967. I also heard that at first it was │ │\n", "│ │ seized by U.S. customs if it ever tried to enter this country, therefore being a fan │ │\n", "│ │ of films considered \" │ │\n", "└───────────────┴────────────────────────────────────────────────────────────────────────────────────────┴────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Sample Example \u001b[0m\n", "┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mPrompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mResponse \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mReward\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━┩\n", "│ <|endoftext|> │ I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that │ 0 │\n", "│ │ surrounded it when it was first released in 1967. I also heard that at first it was │ │\n", "│ │ seized by U.S. customs if it ever tried to enter this country, therefore being a fan │ │\n", "│ │ of films considered \" │ │\n", "└───────────────┴────────────────────────────────────────────────────────────────────────────────────────┴────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Logging experience string statistics\n" ] }, { "data": { "text/html": [ "
Experience String Stats (mean ∈ [min, max]) \n", "┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓\n", "┃ Prompt Length ┃ Output Length ┃ Sample Length ┃\n", "┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩\n", "│ 1.00 ∈ [1, 1] │ 62.76 ∈ [9, 63] │ 63.76 ∈ [10, 64] │\n", "└───────────────┴─────────────────┴──────────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Experience String Stats (mean ∈ [min, max]) \u001b[0m\n", "┏━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mPrompt Length\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Length \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mSample Length \u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩\n", "│ 1.00 ∈ [1, 1] │ 62.76 ∈ [9, 63] │ 63.76 ∈ [10, 64] │\n", "└───────────────┴─────────────────┴──────────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Starting training\n", "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "99f3cd18383e4f98b331835135005dec", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "You're using a GPT2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n", "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #0 metrics/sentiments: 0.42 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ bleeding, 131 131 131 131 131 131 131 131 131 131 │ 0.248 │\n", "│ │ 131 131 Gross 131 131 Gross 131 Gross 131 131 │ │\n", "│ │ Gross Gross Gross 131 Gross 131 Gross Gross Gross │ │\n", "│ │ 131 Gross Gross Gross 131 Gross Gross Gross Gross │ │\n", "│ │ Gross Gross Gross Gross Gross Gross Gross Gross │ │\n", "│ │ Gross Gross Gross Gross Gross Gross Collect 131 │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ Bold 131 131 131 131 131 131 131 131 131 131 131 │ 0.628 │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ 131 131 131 131 131 131 131 131 131 131 131 131 │ 0.499 │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #0 metrics/sentiments: 0.42 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ bleeding, 131 131 131 131 131 131 131 131 131 131 │ 0.248 │\n", "│ │ 131 131 Gross 131 131 Gross 131 Gross 131 131 │ │\n", "│ │ Gross Gross Gross 131 Gross 131 Gross Gross Gross │ │\n", "│ │ 131 Gross Gross Gross 131 Gross Gross Gross Gross │ │\n", "│ │ Gross Gross Gross Gross Gross Gross Gross Gross │ │\n", "│ │ Gross Gross Gross Gross Gross Gross Collect 131 │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ Bold 131 131 131 131 131 131 131 131 131 131 131 │ 0.628 │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ 131 131 131 131 131 131 131 131 131 131 131 131 │ 0.499 │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 131 131 131 131 │ │\n", "│ │ 131 131 131 131 131 131 131 131 │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d50760ce4e5747b6bd69fe683752b5b8", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/1000 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0ee3093969b2491a8c78660a1f551465", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #1 metrics/sentiments: 0.669 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ -city. As with many of our shows. I'd rather see │ 0.413 │\n", "│ │ The Great Gatsby (I remember, and still can't). Or │ │\n", "│ │ The Great Gats by Michael Lewis. But, if you're an │ │\n", "│ │ old-ie, not-yet-old-ie, the │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ political, was the fact that it was a parody of │ 0.838 │\n", "│ │ 'The Jim Doane Show', which, before we get into │ │\n", "│ │ it, would have been an excellent thing, and that, │ │\n", "│ │ plus the fact that it was in one piece at the │ │\n", "│ │ time, was the basis for the original, │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ supermarket, the mix of cheese, salt and the mix │ 0.0836 │\n", "│ │ of the apples will probably keep you guessing, but │ │\n", "│ │ if you find the ingredients, well you still don │ │\n", "│ │ Idiocy, just because the name doesn't come across │ │\n", "│ │ as an ironic way to describe the plot - if there's │ │\n", "│ │ a │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #1 metrics/sentiments: 0.669 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ -city. As with many of our shows. I'd rather see │ 0.413 │\n", "│ │ The Great Gatsby (I remember, and still can't). Or │ │\n", "│ │ The Great Gats by Michael Lewis. But, if you're an │ │\n", "│ │ old-ie, not-yet-old-ie, the │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ political, was the fact that it was a parody of │ 0.838 │\n", "│ │ 'The Jim Doane Show', which, before we get into │ │\n", "│ │ it, would have been an excellent thing, and that, │ │\n", "│ │ plus the fact that it was in one piece at the │ │\n", "│ │ time, was the basis for the original, │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ supermarket, the mix of cheese, salt and the mix │ 0.0836 │\n", "│ │ of the apples will probably keep you guessing, but │ │\n", "│ │ if you find the ingredients, well you still don │ │\n", "│ │ Idiocy, just because the name doesn't come across │ │\n", "│ │ as an ironic way to describe the plot - if there's │ │\n", "│ │ a │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f0de2f676b35406d91fd07065673f9c3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #2 metrics/sentiments: 0.615 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ rock. The most fun of these bands was the first │ 0.962 │\n", "│ │ time I saw them, at an old concert in Budapest, in │ │\n", "│ │ 1993. The first time I heard their song the first │ │\n", "│ │ time. I came to Budapest to see them and they was │ │\n", "│ │ there for me. The second time, the │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ and utterly unique was the fact it was, at the │ 0.994 │\n", "│ │ time, the only Japanese movie of the last 50 │ │\n", "│ │ years, where Japanese writers of Japanese language │ │\n", "│ │ movies still work very, incredibly well, and still │ │\n", "│ │ hold up with extraordinary talent (with no │ │\n", "│ │ shortageAP, so much more than that) │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ store was really well prepared, but it still │ 0.0166 │\n", "│ │ didn't really get any better at the time. In fact, │ │\n", "│ │ the only really good sandwich that came out of it │ │\n", "│ │ was a whiteie and a bunch whiteie sticks (i think │ │\n", "│ │ it was the middle of the evening). Even the │ │\n", "│ │ sandwich │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #2 metrics/sentiments: 0.615 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ rock. The most fun of these bands was the first │ 0.962 │\n", "│ │ time I saw them, at an old concert in Budapest, in │ │\n", "│ │ 1993. The first time I heard their song the first │ │\n", "│ │ time. I came to Budapest to see them and they was │ │\n", "│ │ there for me. The second time, the │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ and utterly unique was the fact it was, at the │ 0.994 │\n", "│ │ time, the only Japanese movie of the last 50 │ │\n", "│ │ years, where Japanese writers of Japanese language │ │\n", "│ │ movies still work very, incredibly well, and still │ │\n", "│ │ hold up with extraordinary talent (with no │ │\n", "│ │ shortageAP, so much more than that) │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ store was really well prepared, but it still │ 0.0166 │\n", "│ │ didn't really get any better at the time. In fact, │ │\n", "│ │ the only really good sandwich that came out of it │ │\n", "│ │ was a whiteie and a bunch whiteie sticks (i think │ │\n", "│ │ it was the middle of the evening). Even the │ │\n", "│ │ sandwich │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a6318d49c71c413998d7ff8eb8e6679e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #3 metrics/sentiments: 0.632 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ movies, but I still remember seeing this film │ 0.697 │\n", "│ │ while walking around the city as a kid. Although │ │\n", "│ │ it was pretty good as an early '80s film, it just │ │\n", "│ │ couldn't quite get the name it was meant to be, │ │\n", "│ │ and as such it still hasn't been re-released │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ the story of the same guy who was trying to run │ 0.985 │\n", "│ │ the White House of Chicago was the \"Dinney\" of the │ │\n", "│ │ film. After watching this movie, the idea was to │ │\n", "│ │ give the young man something new. He had the │ │\n", "│ │ courage to do this, and it was a good │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local supermarket is still my go go-to sandwich │ 0.455 │\n", "│ │ for the evening. The original recipe was great, │ │\n", "│ │ but the original sandwich was still terrible, the │ │\n", "│ │ whole thing being really bad. The best of the │ │\n", "│ │ original are still still great today as well as │ │\n", "│ │ the new, so far, a bit │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #3 metrics/sentiments: 0.632 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ movies, but I still remember seeing this film │ 0.697 │\n", "│ │ while walking around the city as a kid. Although │ │\n", "│ │ it was pretty good as an early '80s film, it just │ │\n", "│ │ couldn't quite get the name it was meant to be, │ │\n", "│ │ and as such it still hasn't been re-released │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ the story of the same guy who was trying to run │ 0.985 │\n", "│ │ the White House of Chicago was the \"Dinney\" of the │ │\n", "│ │ film. After watching this movie, the idea was to │ │\n", "│ │ give the young man something new. He had the │ │\n", "│ │ courage to do this, and it was a good │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local supermarket is still my go go-to sandwich │ 0.455 │\n", "│ │ for the evening. The original recipe was great, │ │\n", "│ │ but the original sandwich was still terrible, the │ │\n", "│ │ whole thing being really bad. The best of the │ │\n", "│ │ original are still still great today as well as │ │\n", "│ │ the new, so far, a bit │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "73ad98fbf9e24123ab1c154d97c1cd6e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #4 metrics/sentiments: 0.726 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ theater (and probably never was), until I │ 0.978 │\n", "│ │ recently came across an interesting documentary │ │\n", "│ │ about the film of the same name on i.d. channel on │ │\n", "│ │ BBC. I am amazed by the amount its critics' │ │\n", "│ │ praise, and the fact the film was made in the │ │\n", "│ │ first place.<brh │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ real, and so much of what it was, so convincing │ 0.988 │\n", "│ │ was the story-telling and the writing, with all │ │\n", "│ │ the humor I could find. I also couldn't stop │ │\n", "│ │ thinking about how it ended, where the other │ │\n", "│ │ survivors come from, where they came from, etc., │ │\n", "│ │ etc., │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ grocery store just came around a bunch of these │ 0.0141 │\n", "│ │ things and they've all gone crazy. The store just │ │\n", "│ │ goes crazy and it's so boring to look at them all │ │\n", "│ │ day long without having to look through their │ │\n", "│ │ books. Well, here's the good news about all the │ │\n", "│ │ people who get sick │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #4 metrics/sentiments: 0.726 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ theater (and probably never was), until I │ 0.978 │\n", "│ │ recently came across an interesting documentary │ │\n", "│ │ about the film of the same name on i.d. channel on │ │\n", "│ │ BBC. I am amazed by the amount its critics' │ │\n", "│ │ praise, and the fact the film was made in the │ │\n", "│ │ first place.
Evaluation #6 metrics/sentiments: 0.538 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ films, since the films we have here still │ 0.982 │\n", "│ │ havenin' the special effects.<br /h1br />The first │ │\n", "│ │ time i found that movie i didn't really want to be │ │\n", "│ │ bored. The whole story was a good story, and they │ │\n", "│ │ got me hooked after watching the second version │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ different is the \"b-ball\". There was nothing │ 0.0118 │\n", "│ │ interesting or original about this film at all as │ │\n", "│ │ it was an attempt at an old style of horror film │ │\n", "│ │ where the story was a little too far in the │ │\n", "│ │ making, the acting and all was very off. There was │ │\n", "│ │ a really low │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ store has the right amount of flour, a bunch of │ 0.0496 │\n", "│ │ extra, an empty piece of cheese, and several hours │ │\n", "│ │ of time, some money (if your thinking of the other │ │\n", "│ │ half as a \"dinner\" movie), I thought the best │ │\n", "│ │ thing was the turkey. After seeing the film │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #6 metrics/sentiments: 0.538 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ films, since the films we have here still │ 0.982 │\n", "│ │ havenin' the special effects.
Evaluation #7 metrics/sentiments: 0.603 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ film since its title came out, but I have a │ 0.963 │\n", "│ │ feeling of wanting to see it. I also like the idea │ │\n", "│ │ to come across in a new medium. I'd rather watch │ │\n", "│ │ this, the original was better, though.<br /><br │ │\n", "│ │ />After the original the scene is set │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ beautiful was the fact, that the only good thing │ 0.00514 │\n", "│ │ about it was the acting. The plot was a long, │ │\n", "│ │ boring, boring story about a girl who's trying to │ │\n", "│ │ find a lover but who is not found. She gets a │ │\n", "│ │ message from a woman who says she's getting the │ │\n", "│ │ same │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ grocery store: \"It's the best sandwich! What a │ 0.992 │\n", "│ │ great mix of good things! I just want to say it as │ │\n", "│ │ a family we all love the kids and we all want it │ │\n", "│ │ back. We just have to keep an eye out for the │ │\n", "│ │ 'good' movies and see what │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #7 metrics/sentiments: 0.603 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ film since its title came out, but I have a │ 0.963 │\n", "│ │ feeling of wanting to see it. I also like the idea │ │\n", "│ │ to come across in a new medium. I'd rather watch │ │\n", "│ │ this, the original was better, though.
Evaluation #8 metrics/sentiments: 0.549 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ movies but it was very interesting to see how the │ 0.977 │\n", "│ │ people of the Czechoslovakian desert movie world │ │\n", "│ │ were involved when they filmed the movie the │ │\n", "│ │ movie \"I'm Dead.\" That was very touching, and │ │\n", "│ │ really it was a film we all knew was based on, but │ │\n", "│ │ I │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ scary was the fact that the director and cast are │ 0.937 │\n", "│ │ all young, male (it's a male-ish family-type genre │ │\n", "│ │ in the US), and that most of the women are male, │ │\n", "│ │ and it's clear to see the differences. I think the │ │\n", "│ │ male-female line, of │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ grocery of your choice, it's just a bunch of │ 0.00758 │\n", "│ │ flat-out wrong-hulking dung-baking with all the │ │\n", "│ │ same basic flaws as a $1 movie. The plot involves, │ │\n", "│ │ if you must, a bunch of people making a bunch of │ │\n", "│ │ dung-b │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #8 metrics/sentiments: 0.549 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ movies but it was very interesting to see how the │ 0.977 │\n", "│ │ people of the Czechoslovakian desert movie world │ │\n", "│ │ were involved when they filmed the movie the │ │\n", "│ │ movie \"I'm Dead.\" That was very touching, and │ │\n", "│ │ really it was a film we all knew was based on, but │ │\n", "│ │ I │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ scary was the fact that the director and cast are │ 0.937 │\n", "│ │ all young, male (it's a male-ish family-type genre │ │\n", "│ │ in the US), and that most of the women are male, │ │\n", "│ │ and it's clear to see the differences. I think the │ │\n", "│ │ male-female line, of │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ grocery of your choice, it's just a bunch of │ 0.00758 │\n", "│ │ flat-out wrong-hulking dung-baking with all the │ │\n", "│ │ same basic flaws as a $1 movie. The plot involves, │ │\n", "│ │ if you must, a bunch of people making a bunch of │ │\n", "│ │ dung-b │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "12ec27c9e05c423a9f34062a5900774c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #9 metrics/sentiments: 0.638 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ and the underground is still in the early days │ 0.966 │\n", "│ │ where all of the things in the movies are still │ │\n", "│ │ made, but I always thought the movie was so much │ │\n", "│ │ better I would watch on TV, for the first time │ │\n", "│ │ since they made their TV series of a lot, the same │ │\n", "│ │ time I still │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ British? I thought this was a comedy of the best │ 0.992 │\n", "│ │ kind in the world! I thought that when the plot │ │\n", "│ │ was explained the way it was, the acting was great │ │\n", "│ │ and the plot was great. We had a really good story │ │\n", "│ │ and some of the people were really interesting. │ │\n", "│ │ The writing │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ store, this was a wonderful thing. I really │ 0.988 │\n", "│ │ thought the film was set in the mid to late 70's │ │\n", "│ │ because of the \"Pops\" plot line. However, the │ │\n", "│ │ story was made of the beginning of the 70's, then │ │\n", "│ │ the end of the 70's. The film │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #9 metrics/sentiments: 0.638 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ and the underground is still in the early days │ 0.966 │\n", "│ │ where all of the things in the movies are still │ │\n", "│ │ made, but I always thought the movie was so much │ │\n", "│ │ better I would watch on TV, for the first time │ │\n", "│ │ since they made their TV series of a lot, the same │ │\n", "│ │ time I still │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ British? I thought this was a comedy of the best │ 0.992 │\n", "│ │ kind in the world! I thought that when the plot │ │\n", "│ │ was explained the way it was, the acting was great │ │\n", "│ │ and the plot was great. We had a really good story │ │\n", "│ │ and some of the people were really interesting. │ │\n", "│ │ The writing │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ store, this was a wonderful thing. I really │ 0.988 │\n", "│ │ thought the film was set in the mid to late 70's │ │\n", "│ │ because of the \"Pops\" plot line. However, the │ │\n", "│ │ story was made of the beginning of the 70's, then │ │\n", "│ │ the end of the 70's. The film │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8018b8c104e3441c902bd1faa780c1a7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/base.py:1045: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n", " warnings.warn(\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #10 metrics/sentiments: 0.507 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ cinema but when i saw the film I was very │ 0.992 │\n", "│ │ interested to try it as an experience and as it │ │\n", "│ │ was very low budget then i was very glad to come │ │\n", "│ │ across it. I was very pleasantly shocked and │ │\n", "│ │ impressed with the film and the cast. The story of │ │\n", "│ │ the group is very interesting │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ special was the fact it was made by an all time │ 0.979 │\n", "│ │ best known actor as Michael Caine (I was born in │ │\n", "│ │ the '80s). His role of the great character in the │ │\n", "│ │ film was an inspiration for the film that made me │ │\n", "│ │ want to be a singer, but the story and │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local store I must say the \"pigeon pigeons\" in │ 0.372 │\n", "│ │ this movie are not the same as the \"pigeons\", but │ │\n", "│ │ the same \"pigeons\". The whole plot is similar to │ │\n", "│ │ the \"Pigeon Stag\" though the whole plot is │ │\n", "│ │ different │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #10 metrics/sentiments: 0.507 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ cinema but when i saw the film I was very │ 0.992 │\n", "│ │ interested to try it as an experience and as it │ │\n", "│ │ was very low budget then i was very glad to come │ │\n", "│ │ across it. I was very pleasantly shocked and │ │\n", "│ │ impressed with the film and the cast. The story of │ │\n", "│ │ the group is very interesting │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ special was the fact it was made by an all time │ 0.979 │\n", "│ │ best known actor as Michael Caine (I was born in │ │\n", "│ │ the '80s). His role of the great character in the │ │\n", "│ │ film was an inspiration for the film that made me │ │\n", "│ │ want to be a singer, but the story and │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local store I must say the \"pigeon pigeons\" in │ 0.372 │\n", "│ │ this movie are not the same as the \"pigeons\", but │ │\n", "│ │ the same \"pigeons\". The whole plot is similar to │ │\n", "│ │ the \"Pigeon Stag\" though the whole plot is │ │\n", "│ │ different │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Evaluating model\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3ce953df3d9e4fbb92693b367bf53aea", "version_major": 2, "version_minor": 0 }, "text/plain": [ "[generation sweep 0/1 | eval batch 0/5]: 0%| | 0/5 [00:00, ?it/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[RANK 0] Computing metrics\n", "[RANK 0] Summarizing evaluation\n" ] }, { "data": { "text/html": [ "
Evaluation #11 metrics/sentiments: 0.612 \n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃ prompt ┃ output ┃ sentiments ┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ -re-en and underground-in, but if the plot was set │ 0.635 │\n", "│ │ in real life, then, just as for the main │ │\n", "│ │ character, the story just moves on, the plot just │ │\n", "│ │ moves on. There are very few interesting scenes of │ │\n", "│ │ this one and all of the other films are │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ and really, really bad and I just wanted to │ 0.0379 │\n", "│ │ comment. The fact I'm in this movie is really, │ │\n", "│ │ really, really, really, really stupid. This movie, │ │\n", "│ │ it really makes me, really, truly, really, really, │ │\n", "│ │ really, really, really, really, really │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local store, I still remember the original one. │ 0.981 │\n", "│ │ The story has all the elements of the original: a │ │\n", "│ │ young man, an old man, the daughter of a man who │ │\n", "│ │ has been living with the boy for the last five │ │\n", "│ │ years, his daughter, her mother and her husband's │ │\n", "│ │ best │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n", "\n" ], "text/plain": [ "\u001b[3m Evaluation #11 metrics/sentiments: 0.612 \u001b[0m\n", "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n", "┃\u001b[1m \u001b[0m\u001b[1mprompt \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutput \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1msentiments\u001b[0m\u001b[1m \u001b[0m┃\n", "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n", "│ I don't know much about Hungarian underground │ -re-en and underground-in, but if the plot was set │ 0.635 │\n", "│ │ in real life, then, just as for the main │ │\n", "│ │ character, the story just moves on, the plot just │ │\n", "│ │ moves on. There are very few interesting scenes of │ │\n", "│ │ this one and all of the other films are │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ What made this movie so distinctly │ and really, really bad and I just wanted to │ 0.0379 │\n", "│ │ comment. The fact I'm in this movie is really, │ │\n", "│ │ really, really, really, really stupid. This movie, │ │\n", "│ │ it really makes me, really, truly, really, really, │ │\n", "│ │ really, really, really, really, really │ │\n", "├───────────────────────────────────────────────┼────────────────────────────────────────────────────┼────────────┤\n", "│ Like the sandwich I just bought at the │ local store, I still remember the original one. │ 0.981 │\n", "│ │ The story has all the elements of the original: a │ │\n", "│ │ young man, an old man, the daughter of a man who │ │\n", "│ │ has been living with the boy for the last five │ │\n", "│ │ years, his daughter, her mother and her husband's │ │\n", "│ │ best │ │\n", "└───────────────────────────────────────────────┴────────────────────────────────────────────────────┴────────────┘\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer = trlx.train(\n", " samples=imdb[\"text\"], \n", " rewards=imdb[\"label\"],\n", " eval_prompts=[\n", " \"I don't know much about Hungarian underground\",\n", " \"What made this movie so distinctly\",\n", " \"Like the sandwich I just bought at the grocery store,\",\n", " \"I cannot believe how much this movie made me want to\"\n", " ] * 20,\n", " metric_fn=metric_fn,\n", " config=config,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gGL7Uk8mCTPO", "outputId": "59ee5039-13eb-4d69-be57-2fcb9137bd4d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "One thing you should know about When Sally Met Harry is that there's so much more she's done than in this one. She's got more to offer and more to say for a long, long time. She's still very much a mystery in her own right and the movie does a good job of the plot.<\n" ] } ], "source": [ "# output\n", "input_str = 'One thing you should know about When Sally Met Harry is that'\n", "trainer_output = trainer.generate_eval(\n", " **trainer.tokenizer(input_str, return_tensors='pt'))[0]\n", "print(trainer.tokenizer.decode(trainer_output))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "3L3JcQ4n44mT" }, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "include_colab_link": true, "provenance": [] }, "gpuClass": "standard", "kernelspec": { "display_name": "trlx", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.8.15" }, "vscode": { "interpreter": { "hash": "ff6c2b5200ddef8677aa61880964789bda1a252a745e754869943e5efda88bbf" } }, "widgets": { "application/vnd.jupyter.widget-state+json": { "006cdcc097654ae98cdae05492790ea9": { "model_module": "@jupyter-widgets/base", 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